1420 results about "Data breach" patented technology

The invention relates to the technical field of railway data security, and provides a railway data asset security monitoring and risk early warning method and system, and the method comprises the steps: collecting multi-dimensional feature data of railway data assets, including equipment operation state data, network communication data, data access logs and environment perception data; performing heterogeneous data fusion preprocessing on the multi-dimensional feature data to generate a standardized feature vector; calculating security evaluation indexes of the standardized feature vector based on a dynamic weight distribution algorithm, wherein the security evaluation indexes comprise an integrity evaluation value, an availability evaluation value and a confidentiality evaluation value; generating a multi-level risk early warning signal according to the deviation degree of the safety evaluation index and the dynamic threshold value; and triggering dynamic control of a data asset protection strategy based on the risk early warning signal, wherein the dynamic control comprises access permission adjustment, data encryption strength grading and flow limitation. According to the invention, the safety and reliability of railway data assets can be improved, and data leakage and system fault risks are reduced.

The invention discloses a dynamic authority management system and method based on multi-source salary data integration, and relates to the technical field of salary data management, and the method comprises the following steps: collecting role names, authority boundaries and operation granularity information from a plurality of business systems, and generating structured role semantic ontology entries based on a field-level semantic annotation mode; and disassembling the to-be-mapped role according to the authority dimension, calculating a semantic similarity index between the to-be-mapped role and the standard role based on the role semantic ontology library, and outputting a role consistency scoring matrix. According to the method, the authority difference is identified through the role semantic ontology and the similarity score, the minimum authorization judgment is realized in combination with the rule engine and reinforcement learning, the audit traceability and anomaly detection are guaranteed by using the block chain and the dynamic graph, and finally a strategy self-evolution closed loop is constructed. And the problems of permission mismatching and data leakage caused by role semantic inconsistency are effectively solved.

The invention discloses a mobile collaboration platform data management method, and relates to the technical field of data processing, and the management method comprises the following specific steps: S100, data sensitive grading: calculating sensitivity scores of various types of data in a mobile collaboration platform by using a formula. According to the method, the sensitivity scores of various data are calculated through a formula, and the data are divided according to the sensitivity levels, so that different levels of protection measures are taken for the data with different sensitivity degrees, and meanwhile, through a built-in sensor and a built-in behavior monitoring module, the sensitivity of the data is improved. The operation behavior information, the equipment information and the business process information of the user are collected in real time, the current business scene is intelligently judged, a basis is provided for subsequent privacy protection strategy making, the data processing efficiency is improved, the data security is greatly enhanced, and data leakage and abuse are effectively prevented.

The present application relates to the field of e-government technology, and discloses an e-government privacy and security protection system based on blockchain technology, including a blockchain layer, a data storage layer, a smart contract layer, a user layer, a privacy computing module, a dynamic authorization and trust scoring module, a revocable access permission module, a data tampering detection and reporting module, a decentralized identity management module, and an on-chain audit module; the blockchain layer is used to record and store data and transactions generated in the e-government process; the data storage layer is used to store encrypted sensitive data, and store the corresponding hash value and index on the blockchain. The computing task allocation is dynamically adjusted through the automatic coordination mechanism of the smart contract to ensure computing efficiency and data privacy. At the same time, the on-chain audit tool immediately verifies the compliance of the results and generates reliable decision-making recommendations, thereby providing reliable decision support and avoiding data leakage risks.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention discloses an enterprise registration archive sharing and shareholder right-of-knowledge guarantee system based on full-chain data tracing and auditing, and relates to the technical field of archive management. The system comprises an identity authentication module, an archive storage management module, an archive sharing module, a shareholder right of knowledge guarantee module, a comprehensive statistics module and a system security module. According to the system, full-chain tracing of archive data is realized by utilizing a block chain technology, all change operations are completely recorded, and the transparency of archive management is remarkably improved; the security and authenticity of the shared archive are ensured through electronic signature, watermark and two-dimensional code anti-counterfeiting technologies; shareholders can track stock equity states and change records of enterprises in real time and know enterprise change information in time through a dynamic notification mechanism, so that the right of knowing of the shareholders is comprehensively guaranteed; through dynamic encryption, authority management and abnormal behavior detection, the security of file storage and access is ensured, and data leakage and tampering are effectively prevented.

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

The invention discloses a market supervision data asset management method and system based on compliance and privacy protection, and relates to the technical field of data asset management. According to the method, the privacy compliance strategy is generated according to the metadata label and is packaged into the data capsule, so that the binding of the data and the compliance strategy is realized; the privacy compliance knowledge graph is constructed, and semantic matching and compliance verification are performed in combination with a large language model, so that the semantic consistency and matching degree between a privacy compliance strategy and a supervision regulation are improved, and manual intervention is reduced; a privacy protection processing mechanism is called according to a compliance verification result, and a proper data protection technology is dynamically applied to reduce the risk of data leakage; anonymization processing parameters are optimized before data are published, data distortion processing is implemented, and the privacy disclosure risk is effectively controlled on the premise that data availability is guaranteed; and a data asset value dynamic evaluation model is constructed, the data comprehensive value and the risk level are evaluated, and refined and differentiated management of data assets is supported.

The invention discloses an administrative institution internal control information processing method and system based on big data analysis, and relates to the technical field of big data information processing. And furthermore, by capturing dynamic association characteristics generated by interaction of local contents in the business field and the information, internal control information cross-dimension semantic joint coding representation with context sensing capability is generated, and cross-dimension semantic interaction analysis between the business field and information content semantics is further realized by applying a chain reasoning model. The system can automatically identify potential logical association between the business field and the information content, so that the risk of data leakage can be greatly reduced, and the scientificity of desensitization processing can be improved, thereby enhancing the effectiveness and efficiency of internal control of administrative institutions.

The invention provides a software ecological migration method and system based on intelligent analysis. Wherein a compatibility evaluation database is deployed in a target architecture, an instruction set mapping rule is preset, and migration risk data is generated through semantic verification; dynamically allocating resources through a heterogeneous virtualization scheduling engine, calling a hardware acceleration unit translation defect instruction, and generating virtualization equipment simulation parameters based on the migration risk data; deploying protocol conversion middleware, dynamically analyzing and reconstructing a network protocol data packet, and combining instruction execution delay data to adjust analysis granularity; in cross-architecture transmission, instruction conversion defects are injected into control logic, timing sequence errors are corrected through clock offset compensation, and corrected data are transmitted back to update instruction mapping rules. According to the technical scheme provided by the invention, the data leakage risk is reduced, and zero shutdown in the migration process is ensured.

The invention discloses a financial privacy security alignment method and system based on federated learning and adversarial training, and the method comprises the steps: enabling a plurality of financial institution clients to obtain local financial data, and carrying out the semantic analysis and sensitivity grading; constructing a federated learning framework, performing homomorphic encryption on a local model gradient by adopting Paillier, uploading the local model gradient to an aggregation server for secure aggregation, and generating global gradient update parameters; dynamically selecting a desensitization strategy based on the business scene weight and the data sensitivity, and executing field-level desensitization processing on the financial data; an adversarial sample is injected in local model training, and a task loss function and an adversarial loss function are jointly optimized; and mapping compliance terms into technical rules, and completing privacy protection and compliance alignment. According to the method, data privacy protection is enhanced, the data leakage risk is effectively reduced, and financial data privacy security is ensured. Safe transmission and effective aggregation of data are ensured, and the global model training efficiency and safety are improved. An adversarial sample is injected, and high accuracy and stability are kept.

The invention provides a credible sharing method and system for multi-party industrial data, and relates to the technical field of data processing, and the method comprises the steps that multiple participants encrypt local industrial data through a credible execution space, and generate encrypted data and corresponding data fingerprints; defining a data access rule based on the smart contract; verifying and auditing the data access request, and obtaining an encrypted data authorization certificate after verification and auditing; and under the federated learning framework, the trusted execution space of each data provider executes the privacy calculation task based on the authorization certificate, and outputs a task calculation result and sends the task calculation result to the task requester. According to the method and the device, the technical problem of privacy risk of data leakage or tampering caused by contradiction between multi-party industrial data sharing and privacy protection is solved, the privacy can still be ensured while data sharing is ensured through federated learning and trusted execution space, and the security and the credibility of data sharing are improved.

The invention relates to a health data encryption storage method and device, equipment and a storage medium, and the method comprises the steps: obtaining to-be-processed health data, carrying out the sensitivity analysis and grade division of the health data, and obtaining health data sub-blocks; performing encryption preprocessing on the health data sub-blocks, and performing group data protection according to a preset differential privacy algorithm to obtain encrypted data blocks; generating an initial master key through a hardware security module, and performing derived hierarchical encryption on the encrypted data block to obtain a data encryption key; performing fragmentation processing and regular distributed storage on the data encryption key to obtain a dynamic security key; and performing metadata separation storage on the encrypted data according to the dynamic security key, and performing data permission determination according to a preset role-based access control mechanism to obtain an encrypted isolation database. According to the invention, efficient security protection can be maintained under different application scenes and access permissions, and the risk of data leakage is reduced.

The invention discloses a charging facility data security protection method and system, electronic equipment and a storage medium, and relates to the technical field of information security. The method comprises the following steps: verifying the identity of a vehicle based on a charging request to obtain a verification result; when the verification result is successful, obtaining a charging parameter of a control layer of the target charging pile and a billing parameter of a service layer based on the charging request; generating a first key of a control layer and a second key of a service layer based on the vehicle information of the vehicle and the charging request; performing first encryption processing on the charging parameter based on the first key to obtain first encrypted data, and performing second encryption processing on the charging parameter based on the second key to obtain second encrypted data; and the first encrypted data and the second encrypted data are transmitted to a vehicle terminal of the vehicle after time sequence identifiers are added to the first encrypted data and the second encrypted data. By implementing the technical scheme provided by the invention, the confidentiality of the charging parameter and the billing parameter can be improved, the risks of data leakage, tampering and the like are reduced, and the data security of the charging station is further improved.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

The invention relates to the technical field of Internet of Things security, and discloses an Internet of Things control system and a security early warning method, and the method comprises the steps: firstly collecting equipment basis and real-time information in an Internet of Things system, then evaluating equipment risk, network security and data encryption conditions, setting an early warning threshold according to multi-aspect evaluation results and historical security data, and finally, carrying out early warning. And finally, real-time monitoring is carried out, and early warning is triggered when the risk index exceeds a threshold value. The method has obvious advantages, equipment aging and safety risks can be accurately evaluated, equipment maintenance is planned in advance, and the maintenance cost is reduced; network attacks can be effectively identified, and network security is guaranteed; the data encryption security is enhanced, and the data leakage risk is reduced; early warning is given out in time, and the system response speed is increased; and the equipment safety state can be dynamically updated, the system safety management is continuously optimized, the safety and reliability of the Internet of Things system are comprehensively improved, and the stable operation of the Internet of Things system is promoted.

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

The invention provides a private deployment-oriented multi-modal AI knowledge base construction system. The private deployment-oriented multi-modal AI knowledge base construction system comprises a knowledge storage module, an intelligent document loading module, a document partitioning engine module, a data enhancement engine module, a multi-language semantic vector alignment module and a private deployment module, the knowledge storage module comprises a knowledge authority management sub-module and a knowledge source management sub-module, the knowledge authority management sub-module is used for managing and storing knowledge from different sources, and the knowledge source management sub-module is used for managing electronic documents and multimedia documents; according to the method, intelligent identification, partitioning, vectorization and source file storage can be carried out on different types of electronic files, knowledge graph construction is carried out for specific fields, semantic relevance between texts and topics and key entities is fully considered, the method has wider applicability, higher robustness and controllability, the data leakage risk is effectively reduced, and the method is suitable for popularization and application. The method is suitable for enterprise sensitive data protection and personal user elastic computing power requirements.

The invention relates to the technical field of artificial intelligence, and discloses an enterprise-level large-model agent application system supporting multi-modal collaboration, and the system comprises a user interaction terminal, an agent engine server, a knowledge engine server, a plug-in integration center, and a distributed storage unit. The agent engine server is responsible for intention recognition and task arrangement of a multi-modal input signal, and dynamically loads a differential reasoning strategy based on an environment isolation mechanism. And the knowledge engine server constructs a cross-modal semantic anchor point, analyzes an unstructured document into a tetrad knowledge unit, and realizes accurate recall of images and texts by using a hybrid retrieval algorithm. And the plug-in integration center executes outbound replacement and inbound restoration of the sensitive data through the context-aware dynamic desensitization gateway. According to the method, through a multi-modal semantic association and closed-loop verification mechanism, the problems of low complex document retrieval precision and leakage of external calling data are solved, and the service processing capacity and safety of the system are improved.

The invention relates to the field of data security, in particular to a hard disk data protection and secure transmission system. Comprising a Bluetooth module, a hard disk and a storage channel security encryption chip, the storage channel security encryption chip is in wireless communication with mobile phone end application software through the Bluetooth module for identity authentication, and hard disk access authority unlocking and sensitive data releasing are achieved according to an identity authentication result; the storage channel security encryption chip is connected between the host and the hard disk to realize data encryption and decryption transmission and partition control of the hard disk; the hard disk is divided into a public area, a private area and a hidden area, the public area stores open resources in a plaintext form, the private area stores personal data in a ciphertext form, the hidden area stores sensitive data in a ciphertext form, and the sensitive data are accessed by adopting a private protocol through a special tool. According to the invention, access control, hierarchical management, virus protection and secure transmission functions of hard disk data are realized, and the problem of data leakage caused by loss or theft of storage equipment is solved.

The invention provides a water conservancy information security protection method and system. The method comprises the following steps: S1, edge equipment physical security reinforcement; S2, dynamic encrypted data acquisition; S3, adaptive fragmentation redundancy transmission; a protection module is deployed in a water conservancy sensor and an RTU, equipment vibration, uncovering and environmental parameters are monitored, abnormal physical attacks or environmental overrun are detected, a secret key is automatically erased, a data protection mode is started, data leakage when equipment is physically damaged is prevented, data security of edge equipment is guaranteed, and the safety of the equipment is improved. And in cross-system data interaction, an MPC or federated learning technology is adopted, original data is locally reserved, and a block chain audit log is generated to record a calling behavior, so that data sharing is realized, data is ensured not to be locally out, data privacy is guaranteed, and a data calling tracing basis is provided.

The invention discloses a meteorological data transmission method based on an edge protection gateway algorithm, and belongs to the technical field of data transmission, and the method comprises the steps: building communication connection with meteorological observation equipment through a serial port service controller, carrying out the standardization processing of data, and carrying out the fragmentation storage and backup; generating a random seed by using a hardware random number generator, generating a round key through a key expansion function, performing encryption operation on the grouped data, and calculating a message authentication code; sending a password suite list to establish handshake connection, exchanging a key through elliptic curve operation, segmenting compressed data into records, and adding an authentication tag for transmission; identity authentication is carried out by adopting a digital certificate, and security protection is carried out on data through a firewall state table and a feature matching algorithm. Through the scheme of the application, the security of meteorological observation data transmission can be improved, data leakage, tampering and attacking are prevented, and the security and reliability of meteorological data are guaranteed.

The invention discloses a smart home early warning method and system based on dual authentication, and relates to the technical field of smart home. Four-element data of an early warning event is collected through an edge computing gateway, the four-element data is coded and packaged into a data packet, and the data packet is transmitted to a cache region of the edge computing gateway for further processing; according to the smart home early warning system based on dual authentication, through an equipment identity dual authentication mechanism, the security of equipment access is doubly guaranteed from a physical layer and a logic layer, intrusion of unknown equipment is effectively prevented, the risk of data leakage is reduced, in the aspect of data storage, an on-chain and off-chain collaborative storage mode is adopted, and the security of equipment access is improved. The authenticity and integrity of event key information are ensured by using the non-tampering characteristic of the block chain on the chain, and the security of original data is protected through distributed storage and encryption under the chain, so that the privacy and security of user data are protected comprehensively, and the security of the data of the smart home system is ensured.

A video data loss prevention (vDLP) system that uses machine-learning for protection against data exfiltration of sensitive content across multiple tenants in a cloud-based network. The vDLP system consists of tenants that include end-user devices and a vDLP server. The vDLP server is configured to intercept traffic at an application layer of the cloud-based network and receive a video file from traffic. A viewer for the video file is remote from the cloud-based network. The vDLP server further recognizes text of audio and frames extracted from the video file using a machine-learning engine. The vDLP server then analyzes the frames and text using machine-learning classifiers, enforces policies against machine-learning classifiers for protection against data exfiltration of sensitive content in the video file, and sends a notification away from the cloud-based network upon detection of violation of a policy.

The invention discloses a network information security defense detection method and system, and relates to the technical field of network information security, and the system comprises a website security check module, a downloading security check module, an access security check module and a display execution module. In daily Internet surfing, net friends often encounter network security problems, for example, users may receive phishing mails disguised as banks or download malicious software in untrusted websites, resulting in data stealing. Identity embezzlement and data leakage increase privacy and financial risks, and man-in-the-middle attack may intercept sensitive information through an unencrypted Wi-Fi network. According to the method, security detection including domain name check, certificate verification, content scanning, integrity verification and the like is carried out on websites, downloading programs and external access equipment, so that security risks are found and processed in time; and meanwhile, by adopting a virtual machine isolation technology and behavior monitoring, the protection on malicious programs is enhanced, the security risk of external equipment is evaluated, and the security of a network environment and data is ensured.

The invention relates to an alliance chain-based energy data sharing method and device, and a medium, and the method comprises the steps: collecting and preprocessing energy data based on a TEE, encrypting the data in the TEE, uploading the data to an IPFS, generating a hash value, and binding a data owner identifier; when the verification request is responded, integrity verification, numerical value verification and other operations are executed, and an encryption result is generated and returned to the demand side. After verification is passed, copyright information is embedded according to a data type matching watermarking algorithm, the IPFS is re-encrypted and uploaded, and a shared hash value is obtained. And storing the hash value and the decryption key into the block chain through the consensus node of the alliance chain, and transmitting the hash value and the decryption key to the demand side. And after the demand side decrypts and obtains the data, extracting the watermark and comparing the watermark with the on-chain parameters for verification. According to the method, sharing and verification of the data in an encryption state are ensured, privacy protection and use controllability are considered, and the problems of data leakage, tampering and ownership tracing in traditional sharing are solved.

The invention discloses a medical insurance cost prediction and optimization method based on big data analysis, and belongs to the technical field of medical data processing. The method comprises the following steps: constructing a multi-source data acquisition module, and integrating data of an HIS system, a medical insurance platform and wearable equipment by using an FHIR interface and a block chain; a BERT-BiLSTM-CRF model is adopted to fuse multi-modal data, a dynamic model group containing Transform anomaly detection and LSTM-ARIMA time sequence prediction is established, and the prediction error rate is reduced to 12% (reduced by 28% compared with that of a traditional method); a multi-objective optimization system is designed, medical quality and cost control are balanced based on an improved NSGA-II algorithm, the cost of a single disease is reduced by 18%-25%, and the quality standard reaching rate exceeds 95%; a hybrid cloud and homomorphic encryption module is deployed, and the data leakage risk is reduced by 90%; a policy sandbox system is constructed, medical insurance policy simulation deduction is supported, and the response time is shortened to 72 hours. According to the invention, the problems of data islands, low prediction precision and privacy risks are solved, and the whole-process intelligent management and control of medical insurance fees is realized.

The invention discloses a data cross-industry sharing method, system and device, a storage medium and a program product, and the method comprises the steps: carrying out the recognition and encryption of sensitive information in to-be-shared data, and obtaining sensitive data encryption; the sensitive data and dense data leakage risk is analyzed to configure the access permission level of the user, and a user dynamic access control strategy is formulated in combination with analysis of a current data access request; analyzing a shared service demand based on a user dynamic access control strategy, constructing hidden query requests of a plurality of query items, and performing hidden optimization on each hidden query request to generate an encrypted query request; performing data item identifier matching, data item screening and data item priority ranking based on the encrypted query request and sensitive data densification to obtain a matching result data set; and performing security layer encryption on the data items in the matching result data set and transmitting the data items to the requester, and decrypting the received encrypted data by the requester to obtain decrypted shared data.

The invention discloses a communication data leakage detection and protection method combined with deep learning, and belongs to the technical field of communication security, and the method specifically comprises the following steps: collecting chat records, extracting keywords and emotional tendencies by using a natural language processing technology, inputting a deep learning model, and training based on annotation data, dividing work and private chat records; after a private communication encryption instruction is obtained, a private chat record is segmented into data blocks, hash values are generated and stored in block chain nodes, a symmetric encryption algorithm is used for encryption, and a secret key is customized by an employee; calculating the proportion of the two types of chat records, and starting network flow simulation if the proportion exceeds a threshold value; monitoring an access interface in real time, judging abnormal access according to identity permission, request frequency and the like, extracting a hash value when the access is abnormal, and generating a bait record according to the identity and returning the bait record; and finally, the access risk is assessed through the block chain smart contract, and if continuous abnormality occurs, a data self-destruction program is triggered to encrypt the associated data block and terminate the connection, so that the communication data security is ensured.